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对用于放射性图像分析的混合视觉变压器架构进行系统审查

Ji Woong Kim¹, Aisha Urooj Khan², Imon Banerjee^3,4,5

¹School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.

Journal of imaging informatics in medicine

|January 27, 2025

概括

混合视觉变压器 (ViT) 和卷积神经网络 (CNN) 架构将全球上下文理解与本地特征提取相结合,以改进医学成像分析. 这次系统性审查将这些综合模型进行基准测试,以提高诊断准确度.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

视觉转换器 (ViT) 在远程依赖方面表现出色,而卷积神经网络 (CNN) 在局部特征提取方面表现强.
ViT可以错过对医学异常检测至关重要的细微局部细节,而浅层的CNN则难以理解全球背景.
混合架构旨在将ViT和CNN的优势结合起来,用于全面的医学图像分析.

研究的目的:

系统地审查和评估用于医学成像的混合视觉变压器 (ViT) -CNN架构.
分析建筑变化,合并策略和放射学中的应用.
为了基准性能,效率 (参数,推断时间),并确定新出现的趋势.

主要方法:

按照PRISMA指南进行系统的文献审查.
分析了2020年至2024年9月间发表的34篇文章,重点关注放射学中的混合ViT-CNN模型.
基于架构设计,集成方法,ViT应用程序和效率指标的基准测试.

主要成果:

混合ViT-CNN模型有效地减轻了个人架构的限制,提供了综合的全球和本地特征提取.
基于性能和效率的架构排名列表得出.
该综述综合了基本概念,并突出了医疗成像综合ViT-CNN方法的关键趋势.

结论:

整合ViT和CNN为医疗视觉任务提供了全面的解决方案,如细分,分类和预测.
本综述为未来的研究提供了方向,以优化医疗成像的混合模型.
混合ViT-CNN架构具有在医疗保健中提高诊断准确性和图像分析的巨大潜力.

关键词:

混合架构架构是一种混合架构.图像分析图像分析放射学放射学是一门学科.视觉变压器视觉变压器

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